tcepi
/

mbp_pas_model

@@ -1,199 +1,144 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
+license: mit
+language:
+- pt
 library_name: transformers
+tags:
+- text-classification
+- binary-classification
+- modernbert
+- pytorch
+- transformers
+datasets:
+- tcepi/mbp_pas_dataset
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+- roc_auc
+base_model: answerdotai/ModernBERT-base
+pipeline_tag: text-classification
+model-index:
+- name: mbp_pas_model
+  results:
+  - task:
+      type: text-classification
+      name: Binary Text Classification
+    dataset:
+      name: tcepi/mbp_pas_dataset
+      type: tcepi/mbp_pas_dataset
+      split: test
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9861
+    - name: F1
+      type: f1
+      value: 0.9863
+    - name: Precision
+      type: precision
+      value: 0.9796
+    - name: Recall
+      type: recall
+      value: 0.9931
+    - name: ROC-AUC
+      type: roc_auc
+      value: 0.9988
 ---
+# MBP PAS Classification Model
+Este modelo é um fine-tune do [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) para classificação binária, treinado no dataset [tcepi/mbp_pas_dataset](https://huggingface.co/datasets/tcepi/mbp_pas_dataset).
+## Descrição do Modelo
+- **Modelo Base:** answerdotai/ModernBERT-base
+- **Tarefa:** Classificação Binária de Texto
+- **Linguagem:** Português (pt)
+- **Framework:** PyTorch + Transformers
+## Métricas de Performance
+### Conjunto de Teste
+| Métrica | Valor |
+|---------|-------|
+| **Accuracy** | 0.9861 |
+| **F1-Score** | 0.9863 |
+| **Precision** | 0.9796 |
+| **Recall** | 0.9931 |
+| **ROC-AUC** | 0.9988 |
+| **Specificity** | 0.9789 |
+### Matriz de Confusão
+|  | Predito Negativo | Predito Positivo |
+|--|-----------------|-----------------|
+| **Real Negativo** | 139 (TN) | 3 (FP) |
+| **Real Positivo** | 1 (FN) | 144 (TP) |
+### Relatório de Classificação
+```
+              precision    recall  f1-score   support
+    Negativo     0.9929    0.9789    0.9858       142
+    Positivo     0.9796    0.9931    0.9863       145
+    accuracy                         0.9861       287
+   macro avg     0.9862    0.9860    0.9861       287
+weighted avg     0.9862    0.9861    0.9861       287
+```
+## Uso
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+# Carregar modelo e tokenizer
+tokenizer = AutoTokenizer.from_pretrained("tcepi/mbp_pas_model")
+model = AutoModelForSequenceClassification.from_pretrained("tcepi/mbp_pas_model")
+# Classificar texto
+text = "Seu texto aqui"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1).item()
+print(f"Classe predita: {model.config.id2label[predicted_class]}")
+print(f"Probabilidades: {predictions.tolist()}")
+```
+## Treinamento
+### Hiperparâmetros
+- **Épocas:** 5
+- **Learning Rate:** 2e-5
+- **Batch Size:** 8
+- **Weight Decay:** 0.01
+- **Warmup Ratio:** 0.1
+- **Mixed Precision:** FP16
+- **Optimizer:** AdamW
+### Informações de Treinamento
+- **Tempo Total:** 186.64 segundos
+- **Samples/segundo:** 55.19
+- **Loss Final:** 0.1391
+## Dataset
+O modelo foi treinado usando o dataset [tcepi/mbp_pas_dataset](https://huggingface.co/datasets/tcepi/mbp_pas_dataset).
+## Limitações
+- O modelo foi treinado especificamente para o domínio do dataset MBP/PAS
+- Performance pode variar em textos de outros domínios
+- Recomenda-se avaliar o modelo antes de usar em produção